Quantifying national, state, and oil/gas field methane emissions and trends in the U.S. (2019&ndash;2024) through high resolution inversion of satellite observations

Estrada, Lucas A.; Jacob, Daniel J.; He, Megan; East, James D.; Varon, Daniel J.; Balasus, Nicholas; Hancock, Sarah E.; Sulprizio, Melissa; Bowman, Kevin W.; Worden, John R.; Reidy, Emily; Runkle, Benjamin R. K.

doi:10.5194/egusphere-2026-655

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https://doi.org/10.5194/egusphere-2026-655

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05 Mar 2026

| 05 Mar 2026

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Quantifying national, state, and oil/gas field methane emissions and trends in the U.S. (2019–2024) through high resolution inversion of satellite observations

Lucas A. Estrada, Daniel J. Jacob, Megan He, James D. East, Daniel J. Varon, Nicholas Balasus, Sarah E. Hancock, Melissa Sulprizio, Kevin W. Bowman, John R. Worden, Emily Reidy, and Benjamin R. K. Runkle

Abstract. We quantify trends of U.S. methane emissions at the national, state, and oil/gas field levels for 2019–2024 through high-resolution (up to ~25 km) analytical inversion of TROPOMI satellite observations with the open-source Integrated Methane Inversion (IMI 2.1). We find that total anthropogenic methane emissions (37 Tg a^-1) are 34 % higher in magnitude than reported in the U.S. Environmental Protection Agency (EPA) Greenhouse Gas Inventory (GHGI) that provided prior estimates for the inversion. Oil/gas emissions are 64 % higher than the GHGI, consistent with previous studies. Total emissions are flat over the 2019–2024 period (0.0 ± 1.0 % a^-1) but this total reflects a combination of decreasing emissions from the oil/gas (-1.1 ± 0.9 % a^-1), coal (-2.3 ± 1.3 % a^-1), and rice (-9.1 % ± 2.0 a^-1) sectors, offset by increases in the livestock (1.8 ± 1.3 % a^-1) and landfill (0.5 ± 1.4 % a^-1) sectors. The methane intensity from the oil/gas sector continues its downward trend, from 2.3 % to 1.9 % over the 2019–2024 period, but unlike in previous studies we find that this trend does not simply reflect an increase in production but also a decrease in emissions, demonstrating improved emission management. Over half of total U.S. emissions originate from ten states, most dominated by fuel exploitation. Emission inventories compiled by individual states do not always improve on GHGI state estimates. Methane intensities decrease for all major oil/gas fields except those with declining production.

Received: 03 Feb 2026 – Discussion started: 05 Mar 2026

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RC1:
'Comment on egusphere-2026-655', Anonymous Referee #1, 14 May 2026 reply
General comments:
Estrada et al. present the results of a series of annual optimizations of methane emissions in the continental U.S. using TROPOMI satellite retrievals. This is a key progression from previous satellite inversions of methane emissions in the U.S. that were more limited in horizontal resolution, spatial extent, or time range. The manuscript is written clearly and presents external validation of methane emissions by sector at national and sub-national scales alongside an easily accessible online visualization tool, demonstrating the utility of their software for informing efforts to mitigate methane emissions in the U.S. and beyond. The work would benefit from some additional detail on a couple of their assumptions and comparisons, but overall, this is an impactful contribution to the ongoing discussion of methane emissions in scientific and policy communities.
Specific comments:
Page 4, Figure 1: Averaging kernel sensitivities are shown here, and aggregated by state in Table S1, but are not discussed in the text beyond a definition in Section 2.1. Readers without a background in inverse modeling would benefit from interpretation of where your methods struggle to constrain emissions. Are there locations or sectors whose posterior results may be biased by consistently low sensitivity? Is sensitivity in 2024 representative of the full six years?

Page 7, lines 196-197: To show that this is sufficient for attributing emissions by sector with reasonable accuracy, it would be helpful to include some analysis of co-located emissions and their potential impact on posterior trends or sectoral totals, particularly for smaller sources like rice and U.S. states with relatively few state vector elements like in New England and the Pacific Northwest.

Page 7-8, lines 209-210: Is there any spatial pattern in prior bias, posterior bias, or change in bias when comparing to NOAA sites? Does it match Fig. 2a,b?

Page 8, lines 224-225: There are some differences worth mentioning here in emissions from livestock and coal. Also, how do CONUS totals compare with other studies? It may be interesting to see some of the results in Figure S1 in a format similar to Figure 4 as well. Do the results of previous studies alongside your work show any trend?

Technical corrections:
Table S1: Check units on rightmost column (sensitivity)

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Citation: https://doi.org/10.5194/egusphere-2026-655-RC1
RC2: 'Comment on egusphere-2026-655', Anonymous Referee #2, 05 Jun 2026 reply

Review of "Quantifying national, state, and oil/gas field methane emissions and trends in the U.S. (2019-2024) through high resolution inversion of satellite observations" by Estrada et al., for ACP.
Overall notes: This manuscript describes a modeling study to quantify emissions and multi-year trends of methane over the continental US. There have been many similar studies in the past (many from this same team), but this study combines a higher spatial resolution analysis with multiple years. It also updates the trends relative to other papers, showing a continued decline in overall anthropogenic emissions. It is a well-written document and consists of a thorough high-quality analysis. The sector-based attribution of emissions however is based only on the prior emissions, and while this is noted in the manuscript, it would be nice to see some comments on how this affects the final conclusions in terms of the uncertainty of sectoral attribution. I also visited the custom dashboard and was very impressed - this is the kind of data scientists should be providing to policy makers directly, with state-level emissions provided for multiple years, and easily understood and downloaded.
Specific comments:
L120, super-observations are mentioned here before being defined later in the text. I would even advocate for a separate sub-section on observations and background rather than jumping right into the inversion method, even if it is only a few sentences, just so the reader can quickly understand what observations are being used and how they are averaged, etc. Also, including information about how the observations were averaged into super-observations without requiring the reader to go to another reference would be nice.
L147, here the optimization of the background is mentioned without noting what the prior background condition was, that is stated later in the text. Again, this could go into the same section as the observation detail.
L154: Can the authors comment on how the uncertainty on the trend would differ if it were calculated for each separate inversion and then averaged across the inversions? i.e. this trend does not incorporate the uncertainty from inversion method or use the spread from the 42 inversions in any way, correct?
Table 1: How were the values in Table 1 obtained, e.g., the 10 ppb error on the boundary condition, or the observational error standard deviation. Does the observational error standard deviation include transport uncertainty estimate? How are the values of the regularization parameter obtained (gamma)?
L193: How does the assumption of the sectoral attribution of the prior for each cell affect the conclusions of the study as it relates to oil and gas? is this uncertainty incorporated at all into the formal uncertainties for each sector? (presumably for many gas basins the uncertainty is small, where there are no other co-located large methane sources and the prior sectoral attribution is likely more robust)?
L227: Were posterior estimates only aggregated annually, with no further temporal resolution? Recent studies have pointed to possible seasonality in fossil methane emissions (Hu et al., ES&T), is any such seasonality possible to be determined in this study? Are observations sampled equally across seasons, or is there any seasonality to the availability of observations that may skew the annual result? (i.e. if emissions really are seasonal, would this cause aggregation error in the annual posterior estimates?)
L283: It is not clear to me why the lack of correlation with head count points to manure management as the source of this trend, likely because I am not as familiar with what governs emissions from this sector -- perhaps connect these dots for the reader?
L320: Why specifically was Colorado included? (perhaps just a sentence?) Is it because of the target on emissions they have set?

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Citation: https://doi.org/10.5194/egusphere-2026-655-RC2

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Short summary

We use satellite observations to track U.S. methane emissions from 2019–2024 at national, state, and oil and gas field scales. While total emissions remain stable, oil and gas emissions decline even as production rises, indicating progress in emissions management for the oil and gas sector.


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